Using the Intel® SSSE3 Instruction Set to Accelerate DNN Algorithm in Local Speech Recognition

The main algorithm of speech recognition has changed to DNN (Deep Neural Network). Without internet, the speech recognition service in your mobile devices nearly useless, very few times it can listen to what you said and work.With support for the SSSE3 instruction set on Intel’s CPU, we could easy run a DNN based speech recognition application without the internet. Adding direct SSSE3 support...
作者: 最后更新时间: 2019/03/26 - 16:08

Performance Comparison of OpenBLAS* and Intel® Math Kernel Library in R

Today, scientific and business industries collect large amounts of data, analyze them, and make decisions based on the outcome of the analysis. This paper compares the performance of Basic Linear Algebra Subprograms (BLAS), libraries OpenBLAS, and the Intel® Math Kernel Library (Intel® MKL).
作者: Nguyen, Khang T (Intel) 最后更新时间: 2019/07/06 - 16:40

Using Intel Data Analytics Acceleration Library on Apache Spark*

Apache Spark* ( is a fast and general engine for large-scale data processing.

作者: Zhang, Zhang (Intel) 最后更新时间: 2019/03/11 - 13:17

Baidu Deep Neural Network Click-Through Rate on Intel® Xeon® Processors E5 v4

How do new web sites selling products or services appear at the top of the search list? The key is to use the right keywords that people might use to search for their products or services. Baidu1 is the most popular search engine in China. Ad companies can pay Baidu so that their ads appear at the top of the search list.
作者: Nguyen, Khang T (Intel) 最后更新时间: 2019/07/05 - 14:36

Using Intel® Data Analytics Acceleration Library to Improve the Performance of Naïve Bayes Algorithm in Python*

This article discusses machine learning and describes a machine learning method/algorithm called Naïve Bayes (NB) [2]. It also describes how to use Intel® Data Analytics Acceleration Library (Intel® DAAL) [3] to improve the performance of an NB algorithm.
作者: Nguyen, Khang T (Intel) 最后更新时间: 2019/07/06 - 16:40

Manage Deep Learning Networks with Caffe* Optimized for Intel® Architecture

How to optimize Caffe* for Intel® Architecture, train deep network models, and deploy networks.
作者: Andres Rodriguez (Intel) 最后更新时间: 2019/03/11 - 13:17

Recipe: Optimized Caffe* for Deep Learning on Intel® Xeon Phi™ processor x200

The computer learning code Caffe* has been optimized for Intel® Xeon Phi™ processors. This article provides detailed instructions on how to compile and run this Caffe* optimized for Intel® architecture to obtain the best performance on Intel Xeon Phi processors.
作者: Vamsi Sripathi (Intel) 最后更新时间: 2019/03/21 - 12:40

Nervana’s Deep Learning Course

Nervana has joined Intel

作者: 管理 最后更新时间: 2019/03/20 - 13:15

BigDL: Distributed Deep Learning on Apache Spark*

As the leading framework for Distributed ML, the addition of deep learning to the super-popular Spark framework is important, because it allows Spark developers to perform a wide range of data analysis tasks—including data wrangling, interactive queries, and stream processing—within a single framework. Three important features offered by BigDL are rich deep learning support, High Single Node Xeon...
作者: 最后更新时间: 2019/03/11 - 13:17

Intel® Math Kernel Library for Deep Learning Networks: Part 1–Overview and Installation

Learn how to install and build the library components of the Intel MKL for Deep Neural Networks.
作者: Bryan B. (Intel) 最后更新时间: 2019/03/11 - 13:17